Tag: big data

Using Customer Intelligence to Understand Customers and Give Them What They Want

Posted on by Emily Louise Spencer

Know thy customer, and you will be able to please thy customer. When dealing with consumers, information is often lost in the hustle & bustle of everyday dealings. Few are able to fully utilize the signals their customers give in order to reap the rewards. Customer intelligence is aimed at doing just that.

It’s all about data in this age of e-commerce.

Once you have it, you’re working with first-hand accounts of how your customers wish to be treated, what they want to obtain, and how they think these things are to come about.

But data can be tricky to utilize effectively.

First of all, you have to obtain it. And not in any fishy manners if you want to build trust with your customers. 

Secondly, the information provided by customers won’t be in a set format that’s easy to collate, making it difficult and time-consuming to process.

Finally, it’s very easy for these pieces of information to slip through the cracks and get lost, never making their way to the people who would be able to use them.

So, once you have this data, how do you go about using this valuable resource? The secret lies in the art of customer intelligence.

What is customer intelligence?

Customer intelligence is a catch-all term for analyzing customer data in order to find new ways to conform your business to their wants & needs.

While this might sound simple, it’s actually difficult in practice to achieve such an analysis due to the fact that consumers have different preferences.

Think of it like making coffee just the way someone likes it. 

Sure, there are common factors between all of the cups you might make – coffee, milk, sugar, etc. – but there will be subtle differences that make the difference between a good cup of coffee and a great one. ☕

Customer intelligence can factor in those needs when potential customers approach you and vice versa.

It takes into account various data points such as age, location, habits, and more so that you can work with customers on an individual level. It means using all of the different combinations of these that might crop up when dealing with customers. 

It’s no exaggeration to say that this is enough data to give anyone a headache!

As a consequence, utilizing customer intelligence is always done with the help of specialized software. It’s simply too much data for a human to process by hand in any useful amount of time.

Why is customer intelligence important?

In the age of the internet, and even more so following the onset of the COVID-19 pandemic, e-commerce has become more and more personalized. 

It’s no secret that customers expect a personalized experience when dealing with a repeat seller, as 59% of them admit that it has an impact on their purchase decisions.

The customer experience has become increasingly relevant over the past few decades, with consumers following their hearts instead of cold numbers. 

Many will even select an objectively inferior product or service if they deem the experience they have with the seller to be more pleasant. After all, customers cite bad experiences as their number one reason for switching brands or providers.

And how do you provide good experiences? By understanding their individual desires.

What can customer intelligence do for you?

After talking about how emotions are so crucial to business, it’s time to get down to some cold hard facts (I love my coffee with a hint of irony).

As mentioned, improving the customer experience is an excellent idea that will boost your bottom line.

In the next section, we dive into the details of how customer intelligence makes it possible. While more strategies exist, the ones we’ll lay out definitely pack the biggest punch. ????

Cross-selling & up-selling

It’s often quite rare for people to go to a shop for a single item and actually walk away with only that item. 

There are just so many good deals that you can see, and you just have to try them out, right?

Online, things are different. 

You can’t see the entire store out of the corner of your eye like you can in a brick-and-mortar location. 

In fact, you’ll often only visit those pages directly relevant to the single item you’re looking for.

So, how do you show off your wares? Cross-selling and up-selling.

They’re both methods of encouraging customers to view items other than the one they specifically came to your site for, with cross-selling being concerned with complimentary products and up-selling with upgraded ones.

Essentially, they’re a means of getting a customer to want to spend more.

These can be done through advertisement banners, recommended product sections, and by related product sections on product pages.

The catch? Items often have multiple uses and reasons behind purchases, meaning you won’t necessarily know why an individual is after a certain product. 

This makes cross-selling and up-selling a bit less effective.

But with customer intelligence at your disposal, you’ll have the information you need to nudge individuals toward their next purchase.

For example, a customer may purchase a DDR2 piece of RAM, a common computer part.

They might be after it because it’s what they currently have and need a replacement, in which case advising the DDR3 as an up-sell is valid.

Or, they might be working with a legacy computer, one not compatible with DDR3, in which case it wouldn’t be.

If you know which one is the case, you know which action to take.

Below you’ll see a great example of how Amazon utilizes cross-selling.

Amazon offers both a “for you” section as well as one on trending deals. You can clearly see a theme across the top row of items, showing its effectiveness at showcasing an individual’s tastes.

This dual-focus method ensures that while general items that are enjoyed by many are not ignored when it comes to cross-selling, the individual is also acknowledged.

Customer retention

Consumers have changed the way they operate in recent years, being more willing than ever to switch brands or providers over minor disagreements or small mistakes.

One of the biggest changes we saw was the rise of e-commerce transactions, with people turning more and more to ordering products online. 

This makes the e-commerce customer experience critical for driving revenue. If your layout is confusing and the infrastructure is ancient, users will quickly become frustrated. And when that happens, they will easily leave your site in favor of another.

Seems plausible, right? When emotions run high, decisions are made that otherwise might not have been. 

There’s a very real possibility that customers will leave your website if they don’t get the personalized, easy-to-use experience they want. When they do, that’s another customer lost.

And perhaps worse than just another purchase lost, it may be an existing customer that’s not coming back.

Low customer retention is one of the most damning factors when it comes to e-commerce profits, simply because customers cost much more to obtain than they do to keep.

While customer intelligence won’t solve your infrastructure issues, it will help improve other aspects of the customer experience immensely. With all that information you possess, you’re able to highly personalize the experiences each individual has to a great degree. Be it specific items or even your site’s layout.

Given a choice between these two options:

  • A simple cookie-cutter website that’s rigid and confusing.
  • A personalized experience on a website that responds to the way you use it and makes itself easier to navigate.

Which would you pick?

Channel analytics

When you’re running an e-commerce business, you’re often operating across multiple channels of communication. It’s not enough to simply use one social media platform, for example, as you’ll miss out on selling to those who use others.

But what works on one platform won’t necessarily work on the others. Not just because they operate differently but due to the audience that frequents each channel.

With customer intelligence on your side, you can not only measure customer behavior on an individual level but apply these principles to the different channels of communication you work with.

After all, if you have data on the individuals, it’s not hard to lump those together for group analysis.

Then, you’re able to easily translate the customer intelligence data into a form that applies to the individual channels and analyze it accordingly.

This can give you information on:

  • Different customer behaviors by channel.
  • Effectiveness of customer service in each channel.
  • How specific customer service tactics work with each channel.
  • Your ROI for each channel.
  • Sales tactic effectiveness by channel.

Useful, right? You can even use the information you gain from this analysis to determine whether or not it’s worth keeping a channel of communication open.

You might be thinking, why don’t I just perform an analysis on each channel? Surely that’s just as effective?

Well, yes and no. 

You see, you can always use individual data as building blocks to create channel data, but you can’t do it the other way around.

This means that if you want to see how demographics affect each channel, you’d have to factor that into your data collection methods. 

While that might seem like common sense, sometimes you’ll only think of analyzing a factor after the fact, meaning you’d need to do the whole data collection part all over again. 

All-in-all, customer intelligence can always build up to a bigger picture, which is one of its most useful traits.

Optimization & cost-effectiveness

This one follows on from both customer retention & channel analytics, but it’s also its own thing, so a separate title is due.

On the surface level, increased customer retention means lower costs, and channel analytics means that you can optimize your approaches to each channel.

But it goes deeper than that.

When you deal with customers on the individual level, you’ll need to provide individual experiences. Customer intelligence lets you gain the information you need to provide this in a very short time frame, meaning you don’t waste time and money on ineffective techniques.

Overall, the information that customer intelligence provides means that every aspect of your organization can be streamlined, improved, and cut back when necessary. It cuts right to the heart of what customers want, which is the essence of e-commerce.

Brand loyalty

Loyal customers are hard to come by, but they’re well worth the effort to maintain. 

In addition to the retention benefits mentioned above, loyal customers will act as advocates for your brand. It’s like having your own organic advertising department, except it’s free!

So, what do brand loyalty and brand equity have to do with customer intelligence?

The thing about loyalty is that it doesn’t just come overnight. You need to perform consistently well in order to build up loyalty.

While in face-to-face transactions, you can usually tell how the customer reacts to specific methods and adjust accordingly, you have no point of reference as to how to best approach a customer online.

So, how do you choose the best approach? Well, with customer intelligence, you can make educated guesses using an individual’s data. 

This approach won’t be accurate in the beginning, however as time goes on and you gather more data on an individual, you will be able to adjust your approaches more effectively.

You might be thinking that this sounds like developing a relationship with that individual, and you’d be right. It’s simply done via software, as no human could ever keep up with that many individuals at once.

More effective approaches = more customer satisfaction = more loyalty.

The types of customer intelligence data

Generally speaking, customer intelligence data falls into two types, internal and external. The latter branches out into several other sub-types, but it’s quite straightforward and well worth familiarizing yourself with them.

It’s important to note from the get-go that both internal and external types of customer intelligence mix zero, first, and third-party data.

This means we recommend using both forms to gather as much relevant data as possible, especially as zero and first-party data becomes more and more precious with privacy concerns going up in recent years.

Internal customer intelligence

Internal data is the blanket term used to cover anything generated within your organization.

You can obtain internal data from your databases, point-of-sale systems, etc. The data that you receive from this won’t necessarily be different from that obtained externally, but it can be considered more organic and representative of a person’s true feelings than the data generated by prompted methods.

This data is the data that you don’t have to go out of your way to collect. It’s data that you’ve naturally picked up over the course of an individual’s interactions with you.

External customer intelligence

External data is what you get when you specifically gather customer intelligence data.

This data can be obtained via survey, from cookies, information that a user has been prompted to give to you, etc.

This data is often the more useful of the two types as it fills in the gaps and lets you see why certain methods are preferred, certain lines of communication are more used, etc. 

You can split externally-gathered customer intelligence data into three types, personal, geographic, and attitudinal.

Personal data

Personal data is all about demographics. That can mean:

  • Age.
  • Career.
  • Disability status.
  • Education level.
  • Gender.
  • Income.
  • Marital status.
  • Religion.

All of this is incredibly useful when trying to personalize the experiences you can provide, not the least to avoid making irrelevant or even downright unhelpful changes.

There are many ways in which personalization can go wrong, but the more personal information you have, the greater your chances of making it go right.

Geographic data

This covers anything to do with location. It lets you know roughly where a person is when they buy from you.

Why is this important information? Surely when working online it’s all the same, right?

Well, no. 

Certain tactics and strategies might work well in an urban environment but not in a rural one. Why? Because the people who live in these different places think in different ways.

Different environments create different experiences, which in turn means different habits are developed. While not exactly the same, there will be rough similarities in how people who live in the same city might behave online.

Similarly, there are probably differences between cities, states, and countries that need to be accounted for when drawing up plans.

Different geographies can also mean different delivery times, languages, tones, and more.

Attitudinal data

Attitudinal data is a little trickier to quantify, as it can change over time. 

Effectively, it consists of any information on how an individual perceives your brand and the general emotions they feel towards it.

A useful tactic to gather such data is by going through review data.

This gives you a direct line to the voice of the customer, helping you understand strengths, weaknesses, opportunities, and (what’s the T for swot?)

To complement the review data, you can conduct other market research methods like surveys, questionnaires, and focus groups. These can help you get a more rounded picture of attitudinal data.

The process of utilizing customer intelligence: 5 steps to follow

It’s time to get down and dirty.

When trying to utilize customer intelligence, there are five key steps that need to be taken. It’s important to keep these steps in order and not miss any out, as they’re all necessary to obtain a complete picture.

Keep in mind, however, that you can always cycle back a step if your data is confusing. If it’s hard to analyze, hard to decide what to do with, etc., you might just need more data or data from a different source.

Before we begin going over the steps, though, a brief disclaimer.

Customer intelligence is highly contextual, meaning that when you’re performing it you absolutely need to have your aims in mind.

You can’t just perform customer intelligence for the sake of it, as the algorithms and data collection methods will differ depending on what area of the customer experience you’re trying to take a look at.

That said, let’s begin.

???? Step 1: Sourcing

The first step in the process is to choose your sources.

While each source that you could draw from will give some amount of similar data, there are distinct differences between how they operate and what data you can obtain from them.

You can split sources into three types: transactional, behavioral, and psychographic. We’ll talk a little more about them later on.

???? Step 2: Collection

The second step is data collection. 

Once you have your sources, you need to collect data from them. This can be done via website monitoring, heat maps, surveys, and more.

The data collection methods you should use are heavily dependent on the type of source you’re drawing from, so keep that in mind.

???? Step 3: Categorization

Next, you need to categorize your data.

This step is usually done while keeping the different facets of your organization in mind. If you’re looking to improve a specific area of your business, you should place the most weight on the relevant data.

Data can fall into the following categories:

  • Direct feedback, such as reviews & ratings.
  • Indirect feedback, such as comments & chatter.
  • Inferred feedback, such as history, cookies, and location-based data.

Direct feedback can be seen as a reflection of the customer experience, meaning it’s up to the marketing & customer service departments to use.

Indirect feedback is more broad but generally valuable for marketing & product testing departments.

Inferred feedback is all about website data, so it’s the domain of your dev team & design team.

All of these categories contain useful information, but some are more useful in specific contexts than others.

???? Step 4: Analysis

Once your data is all sorted into neat little packages, it’s time to analyze it.

This step is where customer intelligence software packages really shine. It’s one thing to know how to analyze data in theory, but a whole other ballpark to actually perform it.

Some common analyses methods that come pre-programmed include:

  • Customer lifetime value predictions.
  • Customer behavior modeling.
  • Predictive customer analysis.
  • Dynamic micro-segmentation.
  • Actionable insights.
  • Customer persona modeling.
  • One-to-one insight generation.

By using these pre-existing software packages, you’ll save yourself countless hours of hard work. We’ll discuss some of the platforms to generate customer intelligence available later on, as well as their features, advantages & disadvantages.

???? Step 5: Taking action

Finally, once your data has been analyzed, you need to take action.

This step is the crucial one where a lot of customer intelligence strategies fall apart. You see, in order to take action on your data, you need to be able to use the methods necessary to utilize it most efficiently.

Whether this is integrating new software into your website, adding this information to customer journey maps & workflows, or even altering your marketing campaign approaches entirely to account for different responses, you need to commit to these changes if you plan to get the most out of your data.

Change is scary, we can all agree on that, and many businesses would rather stick with tried and true methods than take a chance on something that may or may not work. 

So why should you act on customer intelligence? Why should you risk your profit margins?

Simply put, if you’re thinking of these actions as entirely new strategies, you need to reframe your perspective on them.

Customer intelligence isn’t about telling you what to do. It’s about finding out what you already do, to some degree at least, that is the most effective. 

When taking action, you’re not altering your direction, merely refining it. 

You can use customer intelligence to measure responses to new methods, that’s true, but the information you gain is useful in all aspects of your organization.

What are the sources of customer intelligence?

As mentioned above, the different sources of your data will grant you different information on customer behavior. 

Selecting your sources is the first step in the customer intelligence process, and making that selection depends heavily on what you’re trying to achieve. 

So, what are the types, then? Well, they generally fall into three types, transactional, behavioral, and psychographic.

Transactional

Transactional data is all about purchase history.

Think back to the last few times you’ve ordered items online. There are probably several of those items that fit a trend or are even repeat purchases. Sound about right?

Purchases rarely take place in a vacuum, and what you buy today is likely going to have an impact on what you buy in the future.

In the same way, what customers have bought from you in the past will show trends that can indicate what they might want to buy next. Using these, you can tailor your recommendations, discounts, etc., to each individual’s tastes. 

If you received a discount offer for a product you were thinking of buying in the future anyway, wouldn’t that tempt you to go through with it?

Behavioral

Behavioral data is concerned with customer behavior. In the realm of e-commerce, that translates to how they behave while using your website, emails, app, etc.

Now, you might be thinking, is it possible to track these factors? Well, yes. 

With emails, I recommend tracking mostly clicks rather than opens. Clicks are a strong indicator of subscribers’ intentions, while opens are much weaker ones. Further, with Apple MPP causing inaccurate open data, it’s best not to rely on this metric as it can lead you to false conclusions.

On your website and app, you can track various metrics such as time on page, viewed products, abandoned pages, and much more. In fact, there’s so much data readily available that it’s best to hone in on your goals before diving into them.

Psychographic

Psychographic data is about customer intentions. 

You can think of it as the underlying reasons behind purchases and what encourages someone to buy certain products.

You can get psychographic data in two ways.

First, there is the direct route where you simply ask them. Customer surveys, questionnaires, preference centers, and reading reviews all fall into this category. 

Remember though, while customers are mostly honest when filling out these forms, they may not remember or even be aware of the full story. Thus, treat these answers wisely.

Secondly, there are indirect indications that can inform you about customer intentions.

Transactional & behavioral data are often the sources that lead to this type of psychographic data, as what they show allows you to infer factors that otherwise might have been missed.

To give an easy example, imagine you’ve just received an order for some hockey equipment. It can be described as:

  • Good quality.
  • All bright red or white.
  • Dispatched to New York.

These facts alone don’t tell you much about why the customer purchased these particular items. However, when you take a look at their purchase history, you find that a previous order was dispatched to Detroit.

Taken together, these two factors indicate that this person might be a fan of the Detroit Red Wings and was motivated to buy these particular items as they resemble the team’s uniform.

Indirectly obtained information can be wrong sometimes, as there can be factors that appear together simply by coincidence. When dealing with a customer for whom you have little information, this is expected, and you can adjust your software accordingly.

As time goes on and more evidence is gathered, you can relax and become more confident in your deductions. 

After all, if it walks like a duck, swims like a duck, and quacks like a duck, it’s probably a duck. ????

Customer intelligence platforms to help you understand up from down

The customer intelligence platform you should use will largely depend on what you intend to do with it. 

Some are built for large-scale enterprises, some smaller, and some scale. There are also key differences in how each platform operates, with some being better than others at certain tasks. 

As you can see in the below chart from SoftwareReviews, users of each platform rate them differently in two different yet equally important aspects, features & vendor experience.

Overall, you should look carefully at each option before you decide, but let’s go through some of the more commonly used ones and assess their capabilities.

Revuze

Not to toot our own horn, but the Revuze platform does a stellar job at gathering and analyzing data, providing you with easy-to-understand reports and insights.

 

Not only that, it does everything in real-time and in a couple of clicks.

 

This means that you can respond to customers’ needs and demands swiftly, allowing you to gain a crucial advantage over competitors.

 

But don’t take my word for it. 

 

Our recent case study with Georgia-based grill innovator Char-Broil tells that story much better.

Adobe Analytics

Adobe Analytics, a part of the Adobe Experience Cloud, has the ability to interface with all other pieces of software within the Cloud. In particular, the AI-powered Adobe Target.

The downside? Like most Adobe products, it’s difficult to interface with software from other providers, so if you already use these, you’ll need to build an interfacing program to translate between the two.

Gavagai Explorer

Gavagai Explorer’s text analytics boasts multilingual features, quite useful for those working across borders. 

It also boasts an API that allows for interfacing with third-party platforms, notably Slack, SurveyMonkey & Zendesk.

Pricing starts at $130 per month, with a limitation of 20 ongoing projects per user.

Graphext

Graphext is a Spanish company that supports six languages in its main version, with another four being in beta versions.

Their seamless translation abilities are particularly useful for those wanting to operate in Europe, Latin America, and South America, as English, Spanish & Portuguese are among the languages that have been fully developed.

Users have noted that Graphext is cloud-based and limited to small or medium businesses due to its capacity limits. The platform is also available to individuals for small use with zero charges.

The downsides? As a small company, Graphext isn’t able to easily respond to queries, only offering a text-based chat solution currently. They’re also fairly new and thus not well established in terms of API integrations.

Microsoft Dynamics 365

Microsoft Dynamics 365 is a Microsoft product line, so you know it’s going to be able to run on almost any Windows system. It’s also available in both cloud and on-site versions.

Dynamics boasts excellent ratings for usability, good ratings for support, and mixed reviews for its user interface options.

As a Microsoft-provided app, it also boasts the ability to interface with dozens of third-party applications. It speaks the same language as your operating system, after all.

One complication is that Dynamics is not one app but a series of twelve applications. Naturally, these all seamlessly work together. However, for those working on mobile devices, this isn’t ideal.

Optimove

Optimove CI is known for its user-friendly interface, flexibility, and easy learning curve.

As an organization founded in 2009, Optimove has had a long time to refine its processes. It’s known for great database organization abilities, as well as for learning exactly what customers want. 

One of their greatest strengths, according to reviewers, is its very visual interface which makes visualizing concepts easy.

Downsides quoted include manual importing of data, issues with integrations, and an inability to delete templates which can quickly leave you swamped in them.

People Pattern

People Pattern comes from a US-based company operating outside of Silicon Valley. It’s rated highly for its data import abilities and its analytics but less highly for support & integrations.

One aspect that sets People Pattern apart from its contemporaries is its highly-rated customization abilities, which users have cited as their main reasons for purchase.

On the flip side, this software is only really useful for small & mid-size businesses or individuals. 

Signal CI Platform

Signal’s main pros are all about integration and scalability. That said, ease of use isn’t quite up to standard with some of the other platforms on this list. 

Signal CI also suffers from dataset size limitations, making it unideal for larger businesses. It more than makes up for this, however, with its Rules Engine feature that allows for automatic data filtering during collection & segmentation.

Overall, a solid choice for anyone from individuals to medium-sized enterprises.

Takeaways

Customer intelligence can be tricky to get to grips with, but once you’re more familiar, you’ll have access to a wealth of customer information.

Ultimately, customer intelligence in e-commerce is driven by the need to personalize and customize the user experience, lest you be left behind by others who do this more effectively. It’s one thing to know what your data says you need to do and another to actually put that into action.

Fortunately, we’ve recently published an article on that very topic, so check out our complete guide to e-commerce personalization next, so you can put your customer intelligence insights into action!

Customer Feedback Analysis: Analyzing & Understanding What Your Customers Are Saying

Posted on by Emily Louise Spencer

Customer feedback is a treasure trove of information with a wealth of insights to offer businesses seeking to improve products and boost revenue. But having feedback is not enough on its own. To understand what customers tell you, employing customer feedback analysis is a must, especially when done at scale. Learn what it means and how to do so successfully.

One of the most impactful approaches you can take to help your business grow is to become a customer-centric company. 

Because not only do customer-centric companies win over the loyalty of their audience for the long term, but they also see the financial benefit of doing so, being 60% more profitable than their competitors. 

In order to truly center your business around your customer, you’ll need to create a culture that commits to listening and catering to the customer’s opinions, thoughts, and needs. And there is no better tool for this than customer feedback analysis. 

By allowing you to systematically and regularly tap into your customers’ opinions, customer feedback analysis enables you to make smarter, more strategic business decisions that help you retain a highly loyal customer base.

In this article, we review everything you need to know about how to successfully implement customer feedback analysis in order to improve business outcomes. 

What is customer feedback analysis?

To understand what customer feedback analysis is and why it’s so important to modern businesses, let’s break the term down into its parts.

The customer part of customer feedback analysis

The customer, or the individual or entity that makes a purchase from your company, is absolutely key to your business’s success. 

As much effort as we may spend in building audiences, courting prospects, and attracting users, it is ultimately the paying customer who directly contributes to your bottom line. 

In understanding your customers, their needs, their pain points, and their opinions of your brand and your product, you’ll be able to make strategic decisions to improve e-commerce customer experience and customer satisfaction, and boost revenue as a result. 

The feedback part of customer satisfaction analysis

Customer feedback is all of the qualitative and quantitative data you receive from your customers that reflect their opinions, preferences, and concerns as they relate to your industry, company, and product. 

You can collect customer feedback through a number of channels, including but not limited to:

  • Emails
  • Surveys
  • Customer service portals 
  • Social media messages and comments
  • Third-party review websites

Feedback may be solicited or unsolicited and can come in several forms, including written comments as well as scores and ratings.

The analysis part of customer feedback analysis

Customer feedback is a form of raw data that contains a multitude of valuable insights for your company.

It is the process of analysis that helps you take this raw data, structure it, and explore it in order to find patterns, identify problems, and extract actionable insights that you can implement in order to make improvements to your product and processes.

???? For more analysis examples, check out our blogs on product performance analysis and competitive product analysis.  

Why should you analyze customer feedback?

Satisfied customers are the lifeblood of a successful business. 

By creating a superior, satisfying customer experience, you are motivating customers to take a number of desirable actions, including

  • Paying more.
  • Recommending your business/products to others.
  • And coming back for repeat purchases.

Indeed, 86% of customers are willing to pay up to 16% more for a superior customer experience. Further, a better retention rate is paramount for businesses seeking growth as existing customers are easier to sell to and are likely to pay more for new products than first-time customers.

Customer feedback analysis holds the key to creating an exceptional customer experience that keeps your customers coming back for more. 

By collecting, analyzing, and acting on the insights you find in customer feedback, you will be able to give customers what they want, address any problems, and gain a reputation as a customer-centric company like massively popular and successful brands.

How do you analyze customer feedback?

While analyzing customer feedback isn’t as simple as throwing numbers at a computer, it doesn’t have to be overly complicated.

In the next section, we break the process into five steps. And when working with dedicated customer feedback analysis tools, you can even skip most of them.

Step 1: Collecting customer feedback

The first step of customer feedback analysis is collecting customer feedback. Here are a few important sources of customer feedback.

Customer calls, chats, and helpdesk emails

This unsolicited form of customer feedback is incredibly valuable, as it represents problems and concerns that customers feel strongly enough about to have actively contacted your company to discuss. 

For this reason, it is best practice to automate a system in which helpdesk emails and customer service call transcripts are automatically added to a feedback database.

In this chatbot conversation from TheKnowledgeGym, a customer shares important feedback, including how they found a brand, how often they use its product, how they use the product, and more.

Surveys

There are a number of customer satisfaction surveys that are commonly used to collect customer feedback. These include:

  • Net Promoter Score (NPS) – This metric measures how likely customers are to recommend your company to a friend or family member. 
  • Customer Satisfaction Score (CSAT) – This metric measures customer satisfaction by directly asking how satisfied customers are with a product, service, or customer service interaction.
  • Customer Effort Score (CES) – This metric measures how hard or easy a customer finds a product or service to use.

You can solicit answers to these surveys through a number of channels, including your website, your mobile app, text message, and email. Individually – and even more so collectively – these surveys can all reveal highly valuable data about customer satisfaction and loyalty. 

NPS score

Here, clothing company Hem & Stitch uses an NPS survey to measure customer loyalty.

Social media comments and messages

Your social media is a fantastic source of customer feedback, with many users these days preferring to reach out to a company with a problem or question over Twitter, Facebook, or Instagram rather than through traditional customer service channels. 

Like with customer calls and emails, it is recommended to automatically forward communication received over social media to your feedback database.

However, not all mentions of you on social media will occur on your account, and not all will tag you. For example, a customer might tweet their opinion about your product on their own personal Twitter account without using any tags or hashtags to alert you to the mention. 

For this reason, it’s wise to engage in an ongoing practice of social listening, or actively monitoring social media in order to find mentions of your brand, even if it’s untagged.

twitter engagement

On Twitter, users are regularly mentioning and posting feedback about brands and products without tagging them.

Online reviews

A final source of customer feedback that we highly recommend tapping into is the online review, a go-to place for customers to share their opinions. You may be able to find reviews of your company in a number of places including:

  • Through your own website.
  • Through general review websites like Yelp and Google Reviews.
  • Through industry-specific review websites like MakeupAlley or Angi.
  • On the app store if you have a mobile app.
  • Reviews on marketplaces such as Amazon, Etsy, and eBay.

amazon reviews bring powerful customer feedback

A customer shares a wealth of important feedback about a product’s color, quality, and sizing through an Amazon review.

Step 2: Structure raw data

Once you have collected customer feedback data, you’ll be the proud owner of a giant mountain of unstructured data. In order to be able to learn something from this information, you’ll have to find a way to organize and categorize it into something more useful. 

First, we recommend going over the data to identify important keywords such as product names, locations, features, etc. Then, you’ll be able to organize the data into categories, which will allow you to identify trends in the data.

Great, now we have neat and ready-to-analyze data. What’s next?

A great way to draw insights from your data is to categorize it. There are endless ways to do that, so it’s important to know what you’re looking for before starting.

Here are some ideas to get you inspired:

  • Topic – If you seek feedback on a specific topic, such as price, delivery speed, or sizing, it is best to categorize data by topic.
  • Sentiment – An approach that is often helpful is to split feedback up by whether it is positive, neutral, or negative. Sentiment analysis is one of the best ways to keep informed on how your product is doing.
  • Type of feedback – Another option is to categorize based on what the feedback is aiming to do. This can be customers that complain, suggest a new product, request a new color, etc.
  • Priority – Some feedback may point to something that needs urgent fixing, like a bug. This is why it can be helpful to organize by priority ranging from less to more urgent.
  • Customer type – You may find useful insights by splitting up feedback by customer type, including paying, trial, non-paying, premium, or VIP/rewards status.
  • Location – If your company has multiple locations or is international, you can categorize feedback by city, state, or country.
  • Product – If you have multiple products, it may be helpful to group feedback by which product it pertains to.

Structuring raw data is something that can be done manually. 

However, not only is it incredibly time-consuming, but error-prone humans are liable to make mistakes every now and then. 

Instead, most companies will rely on some form of technology for this step, whether it’s simpler Excel sheets or more sophisticated dedicated data structuring software.

Step 3: Identifying insightful data

The next step is to separate the insightful data – or new data that either confirms a hypothesis you had or contradicts your prior working assumptions – from non-insightful data, which is data that points to an issue you already knew about. 

When determining whether or not data is insightful, ask yourself:

  • Does this data validate a hypothesis we had?
  • Can this data motivate us to think more critically?
  • Can this data lead us to take action to make an improvement? 
  • Can this data reshape our strategies?

For the rest of the steps of customer feedback analysis, you can set aside the non-insightful data to focus exclusively on the insightful data.

Step 4: Write a customer feedback analysis report

A customer feedback analysis report is a document summarizing the findings of your customer feedback analysis and laying out recommendations for how to follow up. 

How do you write a customer feedback analysis report? We recommend including the following five key sections.

???? Background – Discuss your company’s current state and why you are engaging in customer feedback analysis. Lay out any hypotheses you may have about what you might find in the data. Mention any relevant changes that the company has made recently.

???? Methodology – Explain how you conducted your customer feedback analysis. Review what sources of feedback you used, how you structured your raw data, what tools you used, what your sample size was, and any other relevant details.

???? Results – Display your data in as easy-to-understand a way as possible. Quantitative data can be displayed through graphs and charts. For qualitative data, you may want to choose select quotes to display that demonstrate relevant trends in the data. 

???? Analysis – Discuss the insights that you found in the data. What problems came up, if any? What new features or products did your customers request? What surprised you? Were your hypotheses confirmed or contradicted?

???? Recommendations – Make your recommendations for the next steps. Based on the insights you found, what actions can your company take in order to improve business outcomes? 

Step 5: Act on insights

It’s important to emphasize that customer feedback analysis is only the beginning of a process of ongoing improvement. 

Feedback analysis can serve as an arrow pointing the way in a direction that your company can go in order to improve customer satisfaction and experience. 

It’s up to you to follow the arrow and fix the problems, create new products, and make the tweaks your customers ask for. 

Example of customer feedback analysis

Let’s say you own a start-up that built an app customers can use in order to identify problems in their house plants, such as pests, underwatering, and insufficient sunlight. One of your company’s values is being data-driven, and you aim to become the go-to plant diagnosis app by becoming customer-centric and offering the features customers will prefer most. For this reason, you have decided to implement an ongoing customer feedback analysis process. 

You choose to collect customer feedback through several channels including:

  • App store reviews.
  • NPS, CSAT, and CES surveys.
  • Social media mentions.
  • Customer calls, emails, and chatbot messages.

Once you gather a sufficient sample size of customer data, your business decides to categorize it by topic into the following groups:

  • Feedback about pricing.
  • App bugs.
  • Feature requests.

Once you’ve removed non-insightful data and used an AI tool to group your insightful data into the relevant categories, your business intelligence team generates the following important insights from your data:

  • Trial users aren’t converting into paid users because they feel that the monthly price of the app is too high.
  • Customers are expressing interest in a feature that allows them to browse photos of other plants with the same issue as theirs.

Based on these insights and your BI team’s customer feedback analysis report, your company has decided to lower the monthly subscription fee by 10% and begin work on building the new feature your customers asked for.

Customer feedback analysis and acting on it isn’t some pie-in-the-sky process. It’s something you can do, and your competitors are most likely already doing it.

Customer feedback analysis tools

As we mentioned above, manually conducting customer feedback analysis can be challenging.

The manpower required to go through hundreds or thousands of comments, transcripts, and messages is tremendous, requiring more resources and time than many companies have to spare. 

To help you be more efficient and accurate in your customer feedback analysis, here are some tools worth looking into.

Revuze 

Revuze is a powerful insights tool that uses AI technology in order to automatically collect unstructured customer feedback data from multiple sources, structure it, and organize it into granular, actionable insights. 

Revuze’s machine learning algorithm operates independently to discover relevant topics and trends within the data and analyze sentiment in order to accurately report on customer satisfaction. 

This is a great all-in-one customer feedback analysis solution perfect for implementing a more efficient, scalable customer feedback analysis strategy than what you’d be able to achieve by hand.

Typeform

Typeform is an online survey creator known for being highly intuitive and user-friendly. This tool is great for building and sending out customer satisfaction surveys in order to collect feedback to analyze. 

Power BI

Power BI is Microsoft’s interactive data visualization software that can help you create reports and model the customer feedback data you collect. 

By helping give you a clearer, more visual picture of your data, Power BI can help you better understand it in order to reach valuable insights.

What comes after analyzing customer feedback?

In today’s consumer landscape, it takes more than a great product to win market share. 

The modern consumer seeks an exceptional customer experience from brands that makes them feel seen and understood. 

In order to satisfy customers, win their loyalty, and gain a brand reputation as a company that puts customers first, you need to tune into what customers are saying about you, what they want from you, and what changes they’re asking for. 

Customer feedback analysis is an incredibly powerful process that allows you to keep your finger on your customers’ pulse in order to remain on top of things and continually deliver a delightful experience. 

Best of all? With the right tools, you can automate and optimize these workflows so they can become an integral part of your process without eating up all of your time.

Analyzing your customers’ feedback is a never-ending process. One way to gain insights into the minds of your customers is by conducting focus groups. But to make the most out of them, you’ve got to ask the right questions. Find out more in our blog on the topic. 

Natural Language Processing (NLP) Techniques & Examples

Posted on by Emily Louise Spencer

Data is key to understanding what customers want and need. But sifting through mountains of data and analyzing it can prove a daunting undertaking. That’s where advanced AI tools come in. In this article, we’ll discuss natural language processing techniques (NLP) and share examples of their application, examining how they can drive your growth.

The AI revolution is coming. Today, 35% of companies report using AI in their business, an increase of four percent from 2021. And an additional 42% report that they are exploring ways to begin using AI. 

No matter where you are in terms of readiness to begin adopting artificial intelligence and machine learning in your company, it’s to your organization’s benefit to learn about these emerging technologies and understand how you might be able to apply them in order to improve business outcomes. 

Natural language processing, or NLP for short, is the perfect place to start. 

It’s a powerful application of machine learning technology that can be used in a wide variety of industries for countless applications to help with everything from streamlining business processes to boosting efficiency to improving e-commerce customer experience and brand loyalty.

In this article, we’ll dive into everything you need to know about natural language processing including: 

  • What it is.
  • Its advantages.
  • Relevant techniques.
  • Applications.
  • And, finally, real-world examples.

Let’s start from the top.

What is natural language processing?

Natural language processing is a branch of artificial intelligence that aims to help computers to understand human language input in the form of text or speech. 

NLP combines multiple disciplines, including computation linguistics, machine learning, deep learning, and statistics. 

These technologies work together to essentially give computer software the ability to process and understand human language in the way that another human could, including its meaning, intent, and sentiment. 

NLP technology is used in a variety of applications including:

  • Digital assistants such as Siri.
  • Speech-to-text dictation software.
  • Voice-operated GPS systems.
  • Customer service chatbots.
  • Predictive text.
  • Digital voicemail.
  • Autocorrect.
  • Search autocomplete.
  • Email filters.

Additionally, companies are increasingly using NLP to create enterprise solutions that help businesses simplify processes, increase productivity, and streamline operations.

The benefits of employing natural language processing

It’s standard these days for companies to collect, store, process, and analyze large quantities of numerical data in order to generate valuable insights that can improve results. 

Natural language processing opens up and empowers businesses to make smarter decisions that are based on larger sets of data. Further, this collection and analysis process happens quickly, especially compared to traditional methods.

For this reason, natural language processing has a number of relevant advantages. 

When working with so much data, you’ll be able to generate insights to improve customer experience with the launch of new products.

On top of that, using NLP helps businesses become more efficient by automating work processes that require reviewing or analyzing texts. This frees up employees to work on other needle-moving tasks.

Taken together, you’re bound to see improved productivity, reduced costs, and an uplift in revenue.

The top techniques used in NLP

NLP is a rich field requiring the use of a number of different techniques in order to successfully process and understand human language. Below, we review and define a selection of the techniques commonly used in NLP technology. 

Tokenization 

Also called word segmentation, tokenization is one of the simplest and most important techniques involved in NLP. 

It’s a crucial preprocessing step in which a long string of text is broken down into smaller units called tokens. Tokens include words, characters, and subwords. They are the building blocks of natural language processing, and most NLP models process raw text on the token level.

An example from Medium of how a simple phrase can be broken down into tokens.

Stemming & lemmatization

After tokenization, the next preprocessing step is either stemming or lemmatization. These techniques generate the root word from the different existing variations of a word. 

For example, the root word “stick” can be written in many different variations, like:

  • Stick
  • Stuck
  • Sticker
  • Sticking 
  • Sticks
  • Unstick

Stemming and lemmatization are two different ways to try to identify a root word. Stemming works by removing the end of a word. This NLP  technique may or may not work depending on the word. For example, it would work on “sticks,” but not “unstick” or “stuck.” 

Lemmatization is a more sophisticated technique that uses morphological analysis to find the base form of a word, also called a lemma. 

The difference between how stemming and lemmatization work is illustrated in this image from itnext, using different forms of the word “change.”

Morphological segmentation

Morphological segmentation is the process of splitting words into the morphemes that make them up. A morpheme is the smallest unit of language that carries meaning. Some words such as “table” and “lamp” only contain one morpheme. 

But other words can contain multiple morphemes. For example, the word “sunrise” contains two morphemes: sun and rise. Like stemming and lemmatization, morphological segmentation can help preprocess input text. 

John Hopkins shows morphological segmentation by breaking the word “unachievability” into its morphemes.

Stop words removal

Stop words removal is another preprocessing step of NLP that removes filler words to allow the AI to focus on words that hold meaning. This includes conjunctions such as “and” and “because,” as well as prepositions such as “under” and “in.” 

By removing these unhelpful words, NLP systems are left with less data to process, allowing them to work more efficiently. It isn’t a necessary step of every NLP use case, but it can help with things such as text classification. 

Examples from geeksforgeeks of what short phrases look like with the stop words removed.

Text classification

Text classification is an umbrella term for any technique used to organize large quantities of raw text data. Sentiment analysis, topic modeling, and keyword extraction are all different types of text classification. And we’ll talk about them shortly.

Text classification essentially takes unstructured text data and structures it, preparing it for further analysis. It can be used on nearly every text type and help with a number of different organization and categorization applications. 

In this way, text classification is an essential part of natural language processing, used to help with everything from detecting spam to monitoring brand sentiment. 

Some possible applications of text classification include:

  • Grouping product reviews into categories based on sentiment.
  • Flagging customer emails as more or less urgent.
  • Organizing content by topic.

Sentiment analysis

Sentiment analysis, also known as emotion AI or opinion mining, is the process of analyzing text to determine whether it is generally positive, negative, or neutral. 

As one of the most important NLP techniques for text classification, sentiment analysis is commonly used for applications such as analyzing user-generated content. It can be used on a variety of text types, including reviews, comments, tweets, and articles. 

The Revuze platform employs sentiment analysis to understand how customers feel about various aspects of products. This allows companies to gain insights about consumers’ needs in real-time, and act accordingly to improve overall CX.

In this example from the Revuze platform, you can see how customers rate different aspects of the product.

Topic modeling

Topic modeling is a technique that scans documents to find themes and patterns within them, clustering related expressions and word groupings as a way to tag the set. 

It’s an unsupervised machine learning process, meaning that it doesn’t require the documents it is processing to have previously been categorized by humans. 

A sample NLP workflow from Frontiersin demonstrates how Input text is proprocessed before undergoing topic modeling, which breaks it into several topics. 

Keyword extraction

Keyword extraction is a technique that skims a document, ignoring the filler words and honing in on the important keywords. It is used to automatically extract the most frequently used and essential words and phrases from a document, helping to summarize it and identify what it’s about. 

This is highly useful for any situation in which you want to identify a topic of interest in a textual dataset, such as whether there is a problem that comes up again and again in customer emails. 

Text summarization

This NLP technique summarizes a text in a coherent way, and it’s great for extracting useful information from a source. While a human would have to read an entire document in order to write an accurate summary of it, which takes quite a bit of time, automatic text summarization can do it much more quickly.

There are two types of text summarization:

  • Extraction-based – This technique pulls key phrases and words from the document to make a summary without changing the original text.
  • Abstraction-based – This technique creates new phrases and sentences based on the original document, essentially paraphrasing it.

An example from the Microsoft tech community of how the two types of text summarization work.

Parsing

Parsing is the process of figuring out the grammatical structure of a sentence, determining which words belong together as phrases and which are the subject or object of a verb. This NLP technique offers additional context about a text in order to help with processing and analyzing it accurately. 

This is how parsing might work on a short sentence.

Named entity recognition

Named entity recognition (NER) is a type of information extraction that locates and tags “named entities” with predefined keywords such as names, locations, dates, events, and more. 

In addition to tagging a document with keywords, NER also keeps track of how many times a named entity is mentioned in a given dataset. NER is similar to keyword extraction, but the extracted keywords are put into predefined categories.

NER can be used to identify how often a certain term or topic is mentioned in a given data set. For example, it might be used to identify that a certain issue, tagged as a word like “slow” or “expensive,” comes up again and again in customer reviews. 

A sample by Shaip of how named entity recognition works. 

TF-IDF

TD-IDF, which stands for term frequency-inverse document frequency, is a statistical technique that determines the relevance of a word to one document in a collection of documents. It works by looking at two metrics: the number of times a word appears in a given document and the number of times the same word appears in a set of documents. 

If a word is common in every document, it won’t receive a high score, even if it appears many times. But if a word frequently repeats in one document while rarely appearing in the rest of the documents in a set, it will rank high, suggesting it is highly relevant to that one document in particular. 

Natural language processing applications 

NLP is a quickly developing technology with many different applications for organizations of every kind. Some of the different ways a business can benefit from NLP include:

  • Machine translation – Using NLP, computers can translate large amounts of text from a target to a source language, which can be used for customer support, data mining, and even publishing multilingual content.
  • Information retrieval – NLP can be used to quickly access and retrieve information based on a user’s query from text repositories such as file servers, databases, and the internet.
  • Sentiment analysis – This NLP technique can be used to monitor brand and product sentiment to help with customer service and product sentiment, among other applications.
  • Information extracting – This process, which includes retrieving information from unstructured data and extracting it into structured, editable formats, can be used for business intelligence, including competitive intelligence.
  • Question answering – Question answering uses NLP to give an answer to a question asked in natural human language and can be used for chatbots and customer support.

Natural language processing examples

Here are just a few more concrete examples of ways an organization might apply NLP to its business processes.

NLP in ChatGPT

One of the most popular recent applications of NLP technology is ChatGPT, the trending AI chatbot that’s probably all over your social media feeds. ChatGPT is fueled by NLP technology, using a multi-layer transformer network to generate human-like written responses to inquiries submitted in natural human language. ChatGPT uses unsupervised learning, which means it can generate responses without being told what the correct answer is. 

ChatGPT is an exciting step forward in the application of NLP technology for businesses and individuals alike, with many saying it can rival even Google. Possible uses for ChatGPT include customer service, translation, summarization, and even content writing. 

NLP for customer experience analytics

Using NLP for social listening and customer review analysis can lead to tremendous insight into what customers are thinking and saying about a brand and its products. With sentiment analysis and text classification, companies can:

  • Understand general sentiment about the brand – Does the public feel positively or negatively about us? 
  • Identify what customers like and dislike about a service or product.
  • Learn what new products customers might be interested in.
  • Know which products to scale and which to pull back on.
  • Discover insights that can be used to improve customer experience and boost customer satisfaction. 

For example, let’s say spicy chocolate brand Shock-O just released a new Popping Jalapeno Chocolate and wants to get a sense of whether or not customers like it. Shock-O can use an NLP-powered tool to analyze customer sentiment and learn what people are saying about the Popping Jalapeno Chocolate, whether they speak about it positively or negatively, and what themes come up again and again in reviews of this product. 

All of this information can then be used to determine whether to continue producing Popping Jalapeno Chocolate, whether to increase or decrease its production of it, whether to make it spicier or less spicy, etc. 

NLP for customer service

90% of customers believe that it is essential or very important to receive an immediate response when they have a question. Yet human customer service representatives are limited in availability and bandwidth. 

This is just one reason why NLP-powered chatbots are growing in popularity. By being able to properly understand and analyze customer inquiries, chatbots can offer the necessary answers to questions, helping to improve customer satisfaction while cutting down on agents’ workload.

NLP can also be used to process and analyze customer service surveys and tickets in order to better understand what issues customers are having, what they’re happy with, what they’re unhappy with and more. All of this serves as crucial data for boosting customer happiness, which will, in turn, increase customer retention and improve word-of-mouth.

NLP for recruitment

HR professionals spend countless hours reviewing resumes in order to identify suitable candidates. NLP can make this process much more efficient by taking over the screening process and analyzing resumes for certain keywords. 

For example, you might set up an NLP system to flag any resume that uses the word “Python” or “leadership” for a human to review later on.

This can increase the likelihood of finding strong candidates, helping an organization fill open positions more quickly and with better talent. What’s more, it can also free up HR professionals’ time to focus on tasks that require more strategic thinking.

Conclusion

The idea that data has important insights to offer companies has been widely accepted, leading businesses to invest in various business intelligence technologies in order to improve their processes and offerings. 

But if your organization is only mining numerical data, you’re missing out on a wealth of valuable information to be found in unstructured human language-based data. 

Natural language processing is a powerful technology allowing text and words to be analyzed as efficiently as numbers can. By learning about and investing in NLP, you’ll be able to achieve a number of desirable outcomes, including streamlining processes, improving brand reputation and loyalty, and ultimately boosting revenue.

The next step would be taking these actionable insights and using them to further drive CX with e-commerce personalization.

 

How to Implement E-commerce Personalization and Revolutionize Your Brand

Posted on by Emily Louise Spencer

It’s no secret that nowadays, customers expect and demand a more personal touch from companies they do business with. That’s why e-commerce personalization is essential for the success and growth of your business. Whether you’re looking to get started with personalization or to improve its effectiveness, this guide is for you. 

In the age of the “For You Page” and “Based on your previous interaction” messages, a one-size-fits-all approach to e-commerce simply won’t do. Because the modern consumer doesn’t just want personalization – they demand it. 

According to McKinsey, ​​71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when they don’t get them. That’s why companies that can deliver personalization generate 40% more revenue than those that can’t. 

The bottom line? 

In today’s commercial landscape, personalization is a must for e-commerce companies that want to capture market share and grow their revenue. 

Those who don’t offer personalization will fall behind. 

But those who do will reap the rewards.

This article will offer a complete guide for companies who want to get familiar with personalization and for those that seek to improve it.

We’ll share insights on what it is, why you need it, and how to do it, along with inventive e-commerce personalization examples. 

Let’s dive in. 

What is e-commerce personalization?

E-commerce personalization is the practice of using real-time zero and first-party customer data to display dynamic content specific to the customer. 

A range of user data can be the basis of e-commerce personalization, including their:

  • Demographics. 
  • Browsing history.
  • Past purchases.
  • What device they’re on. 
  • And more. 

In this personalized example, Mother Earth Products uses subscribers’ date of birth to offer discounts on their special day

E-commerce personalization can be delivered across multiple channels, including on a website, in an app, and through emails and text messages. Further, it can be presented to any lead, no matter where they are in the sales funnel.

The truth is that personalized e-commerce is already pretty dominant, and you’ve most likely seen it all over the place, like in:

  • Personalized site layouts.
  • Personalized product recommendations.
  • Personalized messages.
  • Redirects to a geographical site with geo-located offers.
  • Cart abandonment emails.
  • Reminders to re-order a product.
  • Personalized offers.

When done well, a personalized e-commerce experience can feel like magic. 

Each customer is given a VIP treatment customized to their needs, leading them on a journey from brand awareness to product discovery to repeat purchasing.

Seven benefits of personalization in e-commerce 

E-commerce personalization offers many crucial benefits to both customers and businesses. It’s an incredibly effective tool that it has become an absolute must-have for any company.

Let’s review what it can do for you and your shoppers.

Sales and conversions

First and foremost, e-commerce personalization can help you generate more sales than ever. 

A whopping 88% of online shoppers are more likely to continue shopping on an e-commerce website that offers a personalized experience.

This means that personalization is a key tool that can be used to convert visitors into paying customers, with everything from personalized recommendations to special discounts helping to increase conversion rates. 

Indeed, it’s reported that conversion rates increase considerably with the number of personalized page views, with conversion rates doubling from 1.7% to 3.4% when a visitor views three pages of personalized content as opposed to two. 

Customer experience

E-commerce personalization also has the added benefit of improving the e-commerce customer experience. 

By using data to get a clearer picture of your customers’ needs and pain points, you can better cater to individual customers, meeting their preferences throughout the entire customer journey.

Creating an experience in which customers only see what’s relevant to them, don’t have to re-input their payment information each time they make a purchase, and are notified about relevant promotions they’d want to know about – just to name a few things – all contribute to a more seamless, enhanced customer experience. 

Songkick uses personalization to notify subscribers about relevant events based on preferences and location.

Insights about customers

All of the data and important insights about your customers that you collect for the purpose of e-commerce personalization are valuable far beyond your personalization strategy. 

Remember, customers will be happy to share their data with you if treated with care. Use these data-collection efforts and leverage them into insights that will serve customers – all the way from improving your products to personalized marketing tactics. 

Customer service

That’s not to mention the fact that all of the data you capture can also be used for customer service to resolve issues more quickly and offer better solutions to any problems that might arise.

For instance, you may choose to offer special offers and rewards to VIP customers with a particularly high customer lifetime value, or you can use a customer’s location data to give them more accurate shipping time estimates. 

The latter can help avoid the issue that 42% of online customers face, where a product takes longer to be delivered than what was promised at the time of purchase. 

Brand loyalty

By giving your customers the kind of tailored experience they want and showing them that you know who they are and what they need, you’re able to capture their loyalty. 

If you can provide a more personalized, seamless customer experience, you can expect your customers to stick with you and continue choosing you over the competition, boosting brand equity. 

Customer retention

That improved customer experience and increased loyalty that personalization can help you achieve? It also does wonders for customer retention. 

And holding onto your customers is incredibly important as it costs between six to seven times more to get a new customer as opposed to keeping the customers you already have.

Competitive advantage & market share

As we mentioned above, customers don’t just expect personalization anymore; they demand it.

By successfully implementing e-commerce personalization, you’ll be able to deliver the experience your customers are looking for, allowing you to keep up with the modern digital landscape. 

To put it frankly, e-commerce personalization is essential to maintaining relevance and market share in today’s climate. 

Brands that don’t deliver will be left behind and passed over in favor of competitors that do. 

How to implement e-commerce personalization

Okay, we’ve made our point. E-commerce personalization is the new frontier, an undeniable necessity. The next step is implementing it, and to do so successfully, you’ll need to follow these steps.

Set your goals

As a starting point, begin by defining what you want to achieve with your e-commerce personalization efforts. 

Not only will this help guide you when you’re making choices about which tools and strategies to use, but it will also help you assess the success of your efforts later on. 

Some potential goals include:

  • Boosting conversions by 0.5%, with the average e-commerce conversion rate being 3.65%
  • Achieving an 80% customer satisfaction rating, with the average customer satisfaction rate in the retail sector being 77% in 2021
  • Increasing customer retention by 5%, with the average repeat customer rate for e-commerce being 28.2%

Map the customer journey

Next, you’ll need to decide which parts of the user experience you want to personalize. 

In order to do so, you’ll need to understand what your users’ journeys look like. From first learning about your brand to making a purchase and leaving a review, what interactions do your customers have? 

Make a map of all of the channels and touchpoints. 

Here is an example of a customer journey map template (well, a table) from Venngage that you can use to get started.

With that in hand, you’ll be able to decide where to implement elements of personalization throughout the customer journey. 

Ask yourself: which moments would benefit from a more contextual experience? 

For example, perhaps you have a high bounce rate from your front page. This might motivate you to try to add more personalization to the homepage of your website to create a customized experience right from the beginning.

Further, maybe you see that customers are generally unhappy with the payment experience leading to a high cart abandonment rate. 

This can indicate that you may need to add an element or personalization to the payment stage of the customer journey. Alternatively, maybe other actions are needed, like reducing form friction or adding trust badges.

Decide what to personalize

Now, you should be ready to decide which personalization methods you want to start with. As we mentioned above, there are a large variety of options for you here. Some popular personalized elements include:

  • Product recommendations – Use a user’s purchase history, location, and demographic information to deliver them recommendations for products they’re likely to be interested in. These can be delivered through email or on various pages on your website.

While proceeding to checkout, Uniqlo offers shoppers various products they might like based on their current purchase.

  • Targeted offers – From first-time purchase discounts to deals on items abandoned in a cart, targeted offers are a highly effective way to get customers to make a purchase. This example from Golden Village displays a Women’s Day promotion exclusively for female users, making this important day even more special.

  • Continuous shopping for return customers – Help a customer pick up right where they left off by displaying items they were previously looking at. In this Shopify example, you can see how continuous shopping would appear through its platform.

  • Dynamic pricing – 21% of e-commerce businesses use this strategy to adjust prices based on a buyer profile, demographic information, purchase history, and browsing history.
  • Personalized retargeting – Create a more specific retargeting campaign by reminding users of the exact products they were looking at. This Madewell Facebook ad presents shoppers with previously seen products.

Collect data

E-commerce personalization is built on your ability to capture key information about your website visitors. You’ll need to track data points such as:

  • Pages viewed.
  • Time on site.
  • Items favorited.
  • Items added to cart.
  • The last page viewed before leaving the website.
  • Email open rate.
  • Email click-through rate.
  • Past purchases.
  • Average order value.
  • The time interval between purchases.
  • Customer lifetime value.
  • Prior email or social media interactions.
  • Bounce rates.
  • Customer retention rates.
  • Abandoned cart rates.
  • Customer acquisition costs.
  • Sales conversion rate.
  • Net promoter score.
  • Time on site.
  • Transaction path length.

For each personalization method you’ve chosen to implement, think about the data you’ll have to capture and how you might be able to access that data. This can be via a CRM, website analytics, data captured during purchases, etc. 

A note on privacy

While on the topic of data collection, it’s important to broach the topic of the ethics that come with it. 

Although 65% of consumers are willing to share their data to enable a personalized experience, some have gotten increasingly savvy about and even wary of having companies collect, store, and use their data. 

For customers that want to keep data for themselves, there’s a solution. They can simply opt out. 

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) require websites to get users’ consent to have their data tracked. 

That’s what that “cookies” pop-up that you see on any new website you visit is all about. Asking for permission to gather data. 

Not only is complying with these data privacy laws required, but it also helps you build trust with your customers. Those who opt into personalization know that they are willingly volunteering their personal data so you can give them an optimized experience. 

Five e-commerce personalization tools that will drive your growth

Successful e-commerce personalization requires the use of a variety of tools to help with data collection and analysis, as well as personalization campaign implementation. 

Most major e-commerce platforms like Shopify actually offer a relatively robust suite of personalization options for you to play around with.

But there are other options out there for e-commerce personalization software. In fact, there are so many that it can be tough to know what tool to start with. 

When on the lookout for such a tool, keep an eye out for ones that have:

  • Customer segmentation.
  • Multi-channel support.
  • A/B testing.
  • A modern AI engine.
  • Compatibility with the e-commerce platform and any other tools you are using.
  • A strong customer success team to help you make the most out of the product.

Ultimately, like with any other tool, trying out a few demos is a good place to start to help you choose the software that best fits your business and its needs. 

To help you get started, here are a few popular tools you should know about. 

Insider

Insider is an easy-to-use e-commerce personalization platform that connects customer data across multiple channels, uses AI to predict future customer behavior, and creates personalized experiences. Insider can be used across multiple channels, including web, app, email, SMS, and more.

Bloomreach

Bloomreach offers a “Commerce Experience Cloud” with a number of tools powered by a customer data engine, including a content platform, AI-driven search and merchandising, a customer data platform, and marketing automation solutions.

Clerk.io

Clerk.io uses an e-commerce-specific AI technology called ClerkCore to help businesses personalize product recommendations, implement a behavior-based search engine, and integrate email marketing. It also integrates with all of the most popular e-commerce platforms.

Yieldify

Yieldify is another popular end-to-end personalization platform helping businesses deliver personalized experiences to customers no matter where they are in the funnel. They offer a variety of tools, including lead capture forms, social proof campaigns, personalized upsell experiences, and more. 

OptiMonk

OptiMonk is a personalization tool with advanced targeting features leveraging pop-ups to do everything from personalized product recommendations and special offers to stop cart abandonment while boosting upselling and cross-selling.

Revuze

Revuze is not a traditional e-commerce personalization platform, and we don’t claim to be one. That said, the insights you get from the Revuze dashboard will help you better understand what your customers want and need. This, in turn, will help with the development of future personalized products that align with customers’ expectations.

E-commerce personalization examples 

To help you understand what e-commerce personalization can look like in practice, let’s review a few examples of companies successfully using it to create a superior customer experience.

Special offers 

By tracking data and identifying which customers are visiting your website for the first time, you’re able to offer a special discount to first-time visitors, like recipe box company Gousto is doing for first-time visitors.

You can also create special offers on the basis of other useful information. For example, the sporting brand JD targets students with a unique promotion. 

Personalized product recommendations

Perhaps one of the most popular and effective applications of e-commerce personalization is to deliver personalized product recommendations. 

For example, you might offer personalized product recommendations on the basis of demographic information such as age or gender. Or you can personalize on the basis of customers’ prior interactions, showing them products similar to those they have looked at or put in their cart in the past.

Here, Zappos displays personalized shoe recommendations based on items the customer was previously looking at or searching for. By showing other shoes in similar styles, Zappos increases the likelihood of finding a shoe the customer fancies, increasing conversions.

In this email, Uber Eats sends a personalized product recommendation based on food orders the user has made in the past. Can’t blame them for loving their curries.

Suggesting complementary products

Knowing what products your users search and purchase can help you upsell and cross-sell by displaying other products that are similar or could help them “complete the look.” 

A shoulder bag will definitely go nicely with these jeans. Job well done by Farfetch.

Amazon is the master of upselling, as can be seen in the following example. The e-commerce giant shows other items that a person might want to buy along with the iPhone case they are looking at, namely two different types of screen protectors.

Geo-targeted offers and product recommendations

A user’s location is a powerful piece of data that can enable you to provide personalized offers and recommendations that are especially relevant to them. For example, you can show users items that are currently trendy in the state or country they live in. 

Or maybe you use somebody’s location to make sure that you are displaying the correct items for the correct season. 

While you may be featuring coats and scarves on your front page in December, this wouldn’t be relevant to people in the southern hemisphere. Using geographical data, you can personalize your website, so shoppers can browse summer hats and bathing suits instead. 

In the following example, Lesportsac displays more graphic, bolder designs for customers located in Hong Kong, where data analysis revealed that those types of styles are more popular. 

Continuous shopping

Similarly to how you’d leave a bookmark in a book to help you know where to pick up from when you continue reading, you can make a sale even more seamless by showing shoppers the items they were looking at before.

Here, Amazon once again provides a great e-commerce personalization example, this time for continuous shopping recommendations. 

Electric brushes are all the rage for this hygiene-savvy customer.

Cart abandonment emails

Win back users who you may have otherwise lost by sending emails to people who put an item in a cart and then exited your website. Remind them of the item they’re almost missing out on and/or offer a special discount to make the deal even sweeter.

Here, Crocus informs the user that the planter they were looking at before is still available and saved in their cart for purchase. This way, all the customer has to do to complete the purchase is to click the “go to checkout” button.

In this email, TodayTix reaches out to the customer, reminding them to grab the tickets for Mary Poppins they left in their cart, making it easy to complete their purchase. 

E-commerce personalization trends 

While it may be more popular now than ever, e-commerce personalization has proven to be more than just a temporary fad. It’s a new standard for e-commerce companies, helping to deliver an improved customer experience while boosting sales. 

Within the practice, however, there are plenty of trends that can come and go as companies discover new and interesting applications of the data they are collecting. Below, we outline some of the most popular trends in e-commerce personalization today.

Headless personalization

The term “headless personalization” refers to the practice of personalizing content without using a traditional web CMS. Instead, companies can work with a headless API to separate their front-end and back-end systems so that the customer data collected in the back end can be used to personalize the user experience on the front end. 

Essentially, headless personalization allows you to customize each individual user’s content without having to change the design of the entire website. This flexibility is highly useful, if not necessary, for creating the kind of personalized experience today’s customers expect. 

Omnichannel e-commerce personalization

The average person spends almost seven hours a day looking at a screen, including phones, tablets, computers, and more. 

Between websites, apps, all of the social media platforms, emails, texts, and chatbots, customers have no shortage of channels through which to shop and interact with a business. What’s more, 90% of customers expect their interactions across all of these channels to be consistent. 

This is why personalizing only one channel, like your web store, isn’t sufficient. 

An omnichannel approach to e-commerce personalization aims to create a personalized experience across all channels including online, in-store, mobile, and more. 

This is the only way to deliver the personalized experience your customers expect.

Privacy-first and anonymous personalization

With the GDPR making it illegal to use users’ information without their permission, privacy has emerged as a major concern for customers, with a whopping 96% of users choosing to opt-out of allowing apps to track their data in iOS 14.5. This has led to the rise of two important trends in e-commerce personalization.

Privacy-first personalization focuses on collecting only the data that is absolutely necessary to provide an improved, personalized customer experience. It aims to protect customers’ security and win their trust through a genuine interest in respecting each user’s privacy.

Anonymous personalization is the practice of personalizing the e-commerce experience without requiring a user to create an account or log in first. This is especially useful for first-time visitors as well as visitors who opt out of having their cookies tracked, allowing you to create a personalized experience with less data.

Dynamic pricing

While price personalization is still not one of the most common applications of e-commerce personalization, it is rising in popularity as 17% of companies who were not already using it reported plans to implement dynamic pricing in 2021. 

The practice of personalizing prices on the basis of purchase history, browsing behavior, and other data points has the potential for a major boost to revenue. 

But while no customers will balk at receiving a special discount, dynamic pricing does carry the risk of angering customers who feel like they are being charged more than others. 

Businesses who decide to try it out should tread carefully, but price personalization certainly has a lot of potentials. 

Wrapping up

When it comes to e-commerce, personalization is the way forward. It proves to have a number of major benefits for businesses, among them, increasing sales and maintaining market share and relevancy as digital transformation quickly ushers us into a personalized future. 

Successfully implementing personalization into your e-commerce business requires a lot of research, trial-and-error, strategy, and analyses. 

This means the next natural step after implementing eCommerce personalization is to understand how to put all this data into action. And this is what our product performance analysis guide is here to do.

How To Improve Your Product

Posted on by Emily Louise Spencer

How To Improve Your Product

Infographic Improve Your Product

Product improvement is a data-driven process that should not be taken lightly. With the digital revolution well underway and companies seeming to come up with new product modifications on a regular basis, you need to keep on top of features and design preferences if you want to stay competitive.

That being said, there’s such a thing as overdoing it or missing the point. A good example of this is the recent release of Windows 11, which brought many new features, but prevented users from moving the taskbar. This led to a whole host of complaints and caused Microsoft to have to revert the change in late 2021.

Microsoft’s main mistake was to think about features, not usability. It doesn’t matter how brilliant or innovative your designs are if they’re laid out in a way that the users don’t like. People don’t like large amounts of change, and product improvement is no exception to that rule.

What should you do when envisioning new features and upgrades? Think about your customer base and what they use your products for. If you’re dealing with the general population, your products need to be easy to use and understand, at least on the surface level. If you’re looking at a more specialized or niche target audience, you might get away with a less straightforward product that has more available features.

With all this in mind, let’s dive into what happens when you improve your product, and how to go about doing it.

How to get started on improving your product

When you’re starting out, the task of product improvement can seem like a daunting one. Fortunately, there are a few categories of product modifications that you might find yourself looking into, and there are some general rules for each that can be followed in order to get the most out of your alterations.

Exploring new opportunities and avenues
If you want to expand, you’ve got to grow. One of the best ways to do this is by tapping into markets that you haven’t explored before. This kind of product improvement can come in two ways, adding new features to attract new customers, or expanding existing ones so that they reach a wider audience.

New opportunities (the “O” in a SWOT analysis) can be assessed by looking into usage cases of your product that you might have underestimated in the past. That is why completing a thorough SWOT analysis is key when starting to look into product improvement. Let’s say you discover that a consumer-base that does not belong to your go-to audience has been using your product—you would definitely want to tap into it.

It’s also possible to expand into new markets by creating new products specialized for them, but since that isn’t classed as product improvement we won’t talk about it here.

Addressing customer concerns

As evidenced by the Windows 11 example above, upgrades to features aren’t always what your customers want. You need to keep their needs and desires in mind when you implement changes, especially with more specialized products.

This is where listening to the Voice of the Customer comes into play. Only if you monitor your customers’ opinions in online reviews and social media you’ll be able to know their concerns and address them in your product updates.

Changes that address customer concerns are by their nature reactive rather than proactive, happening after you’ve received feedback on your product modifications. For this reason it can be helpful to keep receiving consumer feedback in the testing stages, rather than coming up with a finished design and hoping it goes down well. The term “beta testing” covers this idea, and while it’s mostly synonymous with the software industry it has applications elsewhere too.

Keeping up with the times
All products, but technology especially, can become stale and behind the times. Upgrading your products to match current expectations will keep your customer base interested, as well as have the potential to produce new features. 

Or course this doesn’t just apply to technology. Environmental impact and ease of disposal or replacement is a great customer concern in current times, and older, less green products are being rejected over time by more and more of the population. 

Keeping up with the times is about knowing what’s expected by consumers, which is distinct from what’s desired. If something is taken as an absolute necessity and you fail to provide it, you’re in for a bad time and bad publicity.

Depending on which of these categories your planned improvements fall into, you can adjust your approach to best suit it.

What can be improved in a product

Once you’ve figured out how you want to improve your product, you need to examine it and figure out what can be improved. You need to keep things practical after all, and whether it’s technological limits or manufacturing constraints there will always be something limiting your ability to improve your product to the “ideal” level. 

Cost is another crucial factor in product improvements, as improving revenue and profits are almost always the ultimate aim of any business. There is no clear line that can be drawn at what would be considered worth the cost or not, it’s up to you to decide. 

As success of new features won’t be visible in advance some organizations have adopted what’s called the “minimum viable product” approach, creating the most basic form of a product or feature for release in order to get feedback before committing their full budget to any one idea. 

You should also prioritize features based on customer use, as these ones will see the most return on investment. Customer feedback will tell you which features are the most used, which are problem points and could do with some tweaks, etc. 

What works and what doesn’t

You’re never going to be able to do everything exactly right. There’s always going to be things that have been missed in the ideas stage, unforeseen factors that cause complications and so on. That’s okay, everyone is only human and some mistakes are bound to crop up somewhere along the line. There are several key indicators that your modifications will prove successful, and some that indicate the opposite.

Signs of a great product modification plan:

  • You’re solving a problem or adding something that consumers desire.
  • Your plan is customer-centric.
  • You’ve made your plan flexible enough to get around obstacles.
  • You’ve defined your manufacturing limits and budget properly.
  • You’ve factored the product life cycle into your plan.

The last item is especially important when it comes to product improvement as you need to assess how long this product will last on the market before a new one is required, and thus define what the limit of your improvements will be. Those products with relatively short life cycles such as mobile phones or software will receive far less in terms of upgrades with each iteration than products such as car models which require far more in order to be perceived by consumers as worth buying.

Aside from the opposite of the above, there are a few signs that your product plan needs rethinking:

  • The modifications that you’re making would make your product too similar to another.
  • There are hidden costs that you didn’t account for.
  • The work is taking longer than expected.

None of these by themselves are reasons to drop your plans, merely signs that you need to re-assess them. The last point is especially important, as no matter how good your product is, people aren’t willing to wait forever and will lose interest over time. That being said, you shouldn’t rush the work either or you may end up with faulty products that fail to live up to expectations.

If you’re unsure on any of these points, you need to test, test, test! Alpha versions of products can be checked by your in-house team, with beta versions being made available to a small range of volunteer consumers in return for detailed feedback. Your plan should never be static, since new data means new information and new insights into what your customers want and how you might provide this.

How to analyze product data

Data is complicated, and you’re not going to be able to fully analyze it without software since the sheer volume would overwhelm any human who attempts it. Below you’ll find some of the commonly used analysis tools and a brief description of what they do:

  • Text mining
    Text mining is the process of reading through unsorted text and extracting information that might be useful to you. Generally any software that collects information from raw text will also transform it into an easy to read form, simplifying it by factoring in synonyms and other linguistic quirks.
  • Sentiment analysis
    Sentiment analysis is similar to text mining, except instead of simply pulling words out it looks at the meaning behind the words, or the sentiment that the text is meant to convey. This type of analysis is particularly useful for analyzing internet reviews or other short-form pieces of text that may contain slang or metaphors that simple text mining wouldn’t be able to pick up on
  • Customer sentiment 
    Customer sentiment is a general measure of how customers feel about interactions with you. It’s gathered in a similar way to sentiment analysis, but goes a step further to sum up all the interactions or feedback you receive into a sliding scale of positive to negative. While simplistic, it’s a great way of checking  how your products are doing in the early stages of testing or release. 

Sentimate offers customer sentiment on all products within its database, so if you need to find out what is and isn’t being received well, you can simply sign up and access our huge database of product insights.

How to implement your changes

When it comes to making product improvements, you can’t simply put them in and call it a day. If you don’t advertise your new products or features then consumers won’t be aware of them, and if consumers aren’t aware of them they won’t buy them. Marketing is key here, and you need to keep on your toes and keep your marketing department informed of the changes you’re implementing and what you intend to improve on in the future. 

Key to this, especially if you’re looking at a long process that might take several revisions, is a vision of what you want to produce in the end and how it will be seen by your customer base. If your vision is aligned with that of customer desires, you’ll be able to use marketing to drum up a buzz and get people excited about it. A consumer base who want to buy your products before they’ve even hit the shelves is a valuable asset indeed.

What examples of product improvements can we see today?

There are all sorts of product modifications going on on a daily basis, some successful and some not. Let’s take a moment to look at some of the more successful examples that you might be able to learn from.

  • Koumeican Flat Extension Cords
    Extension cords are a familiar sight to anyone who’s ever worked around any electronics. They do, unfortunately come with a bit of a drawback. Most extension cords simply use regular circular wires, which can lead to you tripping over them and issues pushing anything on wheels past without ripping the cord from its socket. 

Koumeican came up with a brilliant solution to this – a cord that can even go underneath the carpet if necessary, and is flat so as to not be a trip hazard if simply placed on the floor. They looked at an issue that customers were having and solved it in a simple manner, a definite upgrade.

  • The iPhone
    This one is a bit old, but you can’t deny it was innovative. While touchscreen phones, portable music players and devices with mobile internet access had existed before, the original 2007 iPhone was the first to place them all within one device, saving space in consumers pockets and simplifying their device needs. 

What the iPhone did was completely transform the way phone technology was used, as well as expanding Apple’s market reach beyond computers and portable music players to reach a whole new range of consumers. Everywhere you look today you’ll see smartphones and similar devices with multifunctional capacities like tablets and smart watches, all offshoots of the original iPhone’s vision.

Using Sentimate to improve your product

Sentimate offers a wide range of features that can be used to gleam insight into your products. There’s a lot of information you can gather from customer feedback, though it usually comes at great effort. Fortunately, we’ve done it all for you and can give you all the insight you need at the touch of a button.

  • SWOT Analysis
    Strengths, Weaknesses, Opportunities and Threats, together called SWOT, are some of the most valuable pieces of information. Sentimate’s analysis is derived from consumer sentiment in order to divide your product’s aspects into one of the four categories.
    Not every facet of your product needs changing, in fact it’s best to leave the Strengths of your product alone. Opportunities indicate you’re on the right track, whereas Threats mean that you’re barking up the wrong tree when it comes to what your customer base wants. As for Weaknesses, nobody likes those but they do exist and you need to take those into account when deciding how to alter your product next.
  • Competitive Landscape
    With just one click you can compare your products with those of your rivals and see where you lie in the realm of both customer sentiment and review volume.
    You’ll be surprised at the amount of information you can extract from these two figures alone, with high-sentiment low-review count stats being an indicator that your product needs more promotion, whereas the inverse tells you that you need to upgrade the product itself a bit. There’s only one idea place to be on this chart and that’s slap bang in the top right corner, and by analyzing where you stand you can see what you might do to reach that goal.
Infographic Improve Your Product
Infographic Improve Your Product
  • Comparison Tool
    A side-by-side comparison of two products is the best way to assess where you stand in relation to the market leaders (or your closest rival), and Sentimate’s Comparison Tool does just that.
    The data we’ve gathered allows you to view different products and see where they stand, but more importantly why they stand there. When combined with the aforementioned Competitive Landscape tool you’ve got an incredible amount of detail into your products’ standing and how you might improve it.
    From the advantages and disadvantages that consumers see in each product to key metric comparisons that tell you how customers feel, you’ll be sure to know more than you set out to by the time you’re done.
  • Hot Or Not
    Trends can come and go in the blink of an eye, especially in some of the more volatile industries such as fashion or beauty. By using our Hot Or Not tool you can track customer sentiment at the present moment, see what’s rising in popularity within each category and see what’s dropping out of favor.
    By keeping track of rising stars and falling rocks, you’ll be able to tweak your products according to shifting consumer opinions in real-time, even make predictions based on the projected landscape to bring out the next hit product yourself.

How Big Data Is Changing Retail Marketing Analytics

Posted on by Simone Somekh

When it comes to marketing, you can’t talk about it at any length without inevitably involving data. Data is the scientific key to marketing, allowing for verifiable and testable ways of creating great branding and marketing strategies. You can do market research to try and understand the retail consumer all you like,  but without involving data you’ll only have a rudimentary, qualitative view of the picture — not the best when you want to take things more in-depth.

Now you might be wondering, what are retail marketing analytics and what does big data have to do with it? Well, if you’re reading this you probably know what retail marketing is – analytics is the method by which you gain insight on your consumer base and what they’ll best respond to marketing wise. Big data has changed analytics into a more precise field, gathering data on individuals rather than groups and using said data on an individual level – something only made possible by the advent of the digital age.

Big data isn’t just about having lots of data — that’s a common mistake that some businesses can make. It’s about being able to use that data, employing methods such as text mining or AI analysis to leverage the data effectively on both the large and small scales. You can get big profits from big data, but only if you know how to use it effectively.

Changes to Analytics: Big Data Means Smaller Results

With the use of big data and the computing power to process it the focus of analytics has more and more become on the smaller details, things which matter to individuals and specific products you might sell, rather than trying to look at the whole picture. You’ll find several ways big data has changed analytics below, though this isn’t the whole story:

Use Big Data for Personalization

With traditional means of gathering data before the internet age, in addition to merely having offline means of reaching out to your target audience, analytics in the past were often vague and overgeneralised. After all, if you only have a single slot to market with you’ll want to make sure that your advertisement is as relevant as possible to as many people as possible. While a great idea for physical forms of marketing such as billboards and posters, the use of digital platforms where personalization of advertisements is possible means that you need a far more targeted form if you want to achieve the best results. 

With analytics in place, you can use big data to target customers on the individual level, eliminating the need for broad, general advertisement and giving rise to far more specific and in-depth forms that are more likely to be of interest and therefore more likely to bring a return on investment.

Use Big Data for Market Basket Analytics

Getting to know what your customers tend to buy is useful. Knowing what they buy together is just as important, as it points to the consumer mindset and can give away links between products or services that would otherwise not be as noticeable. By using retail consumer data on what products are bought alongside what, you’ll be able to advertise products that would ordinarily fall into very different categories together. It can also be used to create bundles and/or offers for purchasing multiple items, a well established way to bring in more revenue. Getting an insight into the mind of your customer base is one of the best ways to understand them and what they want, with market basket analysis helping a great deal in that regard.

Use Big Data for Market Trend Prediction

Keeping up with the latest trends can be challenging for even the most skilled of personnel. Predicting the upcoming trends that might occur in the near future is something that might seem impossible – for a human, perhaps, but not for AI. Several marketing sectors have embraced sentiment analysis, a technique whereby algorithms can determine emotional context in text and extract information. Given this, market trends can be predicted from messages, news headlines or feedback, as the emotional responses these pieces provoke give an insight into a consumer’s next steps. It’s a technique already being used in the stock exchange, so you know it’s got to be promising.

Use Big Data for Customer Experience Personalization

In the same vein that analytics can determine what ads an individual might respond best to, so too can it predict what interactions with your brand will be favourable with the aforementioned individual. This is less precise than personalization of ads, as the technology to build multi-path platforms isn’t quite as well developed, but given a flexible platform such as an app or a website where multiple methods of completing the customer journey can be implemented it becomes possible to send a customer down a specific path in order to give them the best experience possible. This will not only boost revenue, but increase customer satisfaction and therefore customer loyalty.

Use Big Data for Behind-the-Scenes Operations

Big data doesn’t just help with the customer-facing part of business, in fact by taking into account predictions, trends and other information you can reliably predict what stock you’ll need in order to keep up with demand. No-one likes it when your intended purchase is out of stock, so by keeping up with the latest purchase trends and adjusting your supply chain accordingly you can ensure you almost never run out of critical stock. Other ways data can help is by using product logs or reviews to test product quality and ensure any defects or problems are compensated for in the future, to provide customers with the best product possible.

Conclusion

To sum things up, big data has changed the way organisations think about retail marketing strategies, shifting their focus from broader terms to more individualized approaches – something only made possible by the advent of high-power computing and AI. It’s also allowed for real-time analytics, letting algorithms change facets of web pages or apps at the click of a button as more data becomes available on the targeted consumer.